AI-enabled multi-omics pharmacogenomic modeling guides resistance-aware multitarget optimization of venetoclax therapy in acute myeloid leukemia

Abstract

Acute myeloid leukemia (AML) remains difficult to treat because profound molecular heterogeneity enables rapid adaptive resistance to targeted therapy. We developed an AI-driven pharmacogenomic pipeline that integrates genomic, transcriptomic, epigenetic, and clinical features to predict patient-specific response to venetoclax and translate resistance-associated signals into actionable therapeutic hypotheses. Across internal and external cohorts, the deep learning model showed strong discrimination and generalization (AUROC 0.84-0.90), produced calibrated probabilities, and stratified overall survival independent of standard prognostic factors. Explainable modeling (SHAP) identified mechanistically coherent drivers of sensitivity and resistance: high BCL2 and apoptotic priming favored response, whereas MCL1 upregulation, TP53 disruption, and RAS/MAPK activation were dominant resistance programs. Translating these findings into therapy design, network-based modeling and in silico perturbation prioritized rational combinations expected to block escape routes, including venetoclax plus MCL1 inhibition (e.g., AZD5991-class inhibitors), venetoclax plus FLT3 inhibition (e.g., gilteritinib-class agents) in signaling-driven disease, and venetoclax plus p53-axis modulation (e.g., MDM2 inhibition) in TP53-altered contexts. Structural candidate evaluation using ensemble docking provided supportive drug-target interaction evidence for prioritized dependencies. Together, these results establish a clinically interpretable, resistance-aware AI framework for precision optimization of venetoclax-based combination therapy in AML.